In [6]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [7]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [8]:
stocks = px.data.stocks()
stocks.head()
Out[8]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [9]:
x = stocks.date
y = stocks.GOOG
Google, ax = plt.subplots(figsize=(10,5))
ax.plot(x,y)
ax.set_title('Stock of Google')
ax.set_xlabel('Dates')
ax.set_ylabel('Change')
ax.set_xticks([0,14,29,44,59,74,89,104])
plt.xticks(rotation=90)
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [10]:
x = stocks.date
Stock1 = stocks.GOOG
Stock2 = stocks.AAPL
Stock3 = stocks.AMZN
Stock4 = stocks.FB
Stock5 = stocks.NFLX
Stock6 = stocks.MSFT
Google, ax = plt.subplots(figsize=(15,5))
ax.plot(x,Stock1, label= 'Google', linestyle=':')
ax.plot(x,Stock2, label= 'Apple')
ax.plot(x,Stock3, label= 'Amazon')
ax.plot(x,Stock4, label= 'Facebook')
ax.plot(x,Stock5, label= 'Netflix')
ax.plot(x,Stock6, label= 'Microsoft')
ax.set_title('All of the Stocks')
ax.set_xlabel('Dates')
ax.set_ylabel('Change')
ax.set_xticks([0,14,29,44,59,74,89,104])
ax.legend()
plt.xticks(rotation=90)
plt.show()

Seaborn¶

First, load the tips dataset

In [11]:
tips = sns.load_dataset('tips')
tips['%tips'] = (tips.tip/tips.total_bill).replace()
tips.head()
Out[11]:
total_bill tip sex smoker day time size %tips
0 16.99 1.01 Female No Sun Dinner 2 0.059447
1 10.34 1.66 Male No Sun Dinner 3 0.160542
2 21.01 3.50 Male No Sun Dinner 3 0.166587
3 23.68 3.31 Male No Sun Dinner 2 0.139780
4 24.59 3.61 Female No Sun Dinner 4 0.146808

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [12]:
#Are there differences between male and female and lunch and dinner when it comes to giving tips?
 
g = sns.FacetGrid(tips, col='sex', hue='time')
g.map(sns.histplot, '%tips')
g.add_legend()
#plt.savefig('smoker.png', dpi=200)
plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [13]:
df = px.data.stocks()
stocks_unpivoted = df.melt(id_vars='date',var_name='stock name',value_name='stock value')
fig = px.line(stocks_unpivoted, x='date', y='stock value', color='stock name')
#fig = px.line(df, x="date", y=["GOOG", "APPL", "AMZN", "FB", "NFLX", "MSFT"])
#this does not work on my computer even tough it should!!
#ValueError: All arguments should have the same length. 
#The length of argument `y` is 6, whereas the length of  previously-processed arguments ['date'] is 105
fig.show()

The tips dataset¶

In [14]:
tips = px.data.tips()
tips['%tips'] = (tips.tip/tips.total_bill).replace()
tips.head()
fig=px.histogram(tips,x='%tips',color='time',facet_col='sex')
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [17]:
#load data
df = px.data.gapminder()
df.head()
df_2007_new.head()
Out[17]:
year lifeExp pop gdpPercap iso_num
continent
Africa 104364 2849.914 929539692 160629.695446 23859
Americas 50175 1840.203 898871184 275075.790634 9843
Asia 66231 2334.040 3811953827 411609.886714 13354
Europe 60210 2329.458 586098529 751634.449078 12829
Oceania 4014 161.439 24549947 59620.376550 590
In [16]:
df_2007 = df.query('year == 2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x='pop', y= df_2007_new.index, orientation='h', color=df_2007_new.index, text= 'pop')
fig.update_yaxes(categoryorder='total descending')
fig.show()
In [ ]: